A supply chain plan describes items to be procured and operations to be performed by entities within a supply chain network, in order to deliver products, goods, or services to another entity within the supply chain network. Typically, an entity collects data for the supply chain plan and runs an optimization algorithm during a specified time interval (i.e., daily, weekly), to produce the supply chain plan. However, traditional optimization algorithms require a representation of the complete supply chain plan in memory, which is a latency and is undesirable because it wastes valuable time resources in creating a huge in memory model of the complete supply chain.
In addition, in order to generate an optimal supply chain plan, traditional optimization algorithms traverse the supply chain data by backward and forward propagating along all the paths stored in memory. This traversal is repeated unnecessarily for each pass of the optimization algorithm, which is a latency and is undesirable. In addition, because of the complexity and the amount supply chain data stored in memory, a typical run-time for generating an optimal supply chain plan may be, for example, 10-12 hours. Once the supply chain plan is generated, the entity distributes the supply chain plan to other entities within the supply chain network and then repeats the process during the next specified time interval.
However, shortly after, if not immediately after, the supply chain plan is generated and distributed to the other entities within the supply chain network, various perturbations (i.e., changes or events) often occur that renders the generated supply chain plan infeasible. Such perturbations may include, for example, new demands, changes in capacity, changes in inventory, and the like. In addition, because the supply chain plan is only generated during specified time intervals and the run-time is significant in duration (i.e., 10-12 hours), the entity is not able to re-optimize, or otherwise adjust the supply chain plan to a state of feasibility, until the next specified time interval.
As a result, conventional efforts to re-optimize or otherwise adjust the supply chain plan to a state of feasibility, often involves ad hoc changes that are not optimal. In addition, conventional efforts to reduce the duration of the run-time are disadvantageous, because the speed of optimization is typically incompatible with the quality of the optimization. That is, these conventional efforts to reduce the duration of the run-time adversely affect the quality of the adjustments to the supply chain plan. This inability to reduce the duration of the run-time and to re-optimize or otherwise adjust the supply chain plan to a state of feasibility, based on these perturbations is undesirable.